Perceiving Causality in Character Perception: A Metaphorical Study of Causation in Film

Coëgnarts, Maarten; Kravanja, Peter

doi:10.1080/10926488.2016.1150762

Cited by 10 publications

(8 citation statements)

References 27 publications

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“…They also provide a taxonomy of inferences within affective cognition and also provide a model of the intuitive theory of emotion modeled as a Bayesian network. Another term scholars have used to understand this domain is "emotional causality" [15].…”

Section: Theory Of Emotions: a Look Into Psychologymentioning

confidence: 99%

“…There have been various efforts to understand how humans reason and interpret emotions resulting in various theories of emotion causation based on physiological, neurological, and cognitive frameworks. One such theory is the "emotional causality" [15] that has been developed from the Causal Theory of Perception [27] and Conceptual Metaphor Theory [4,48,37]. "Emotional Causality" refers to the understanding that an experience of emotion is embedded in a chain of events comprising of an (a) outer event; (b) an emotional state; and (c) a physiological response.…”

Section: Introductionmentioning

confidence: 99%

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Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality

Mittal¹,

Mathur²,

Bera³

et al. 2021

Preprint

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We present Affect2MM, a learning method for timeseries emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine the emotional state evoked in clips of movies. Affect2MM explicitly models the temporal causality using attention-based methods and Granger causality. We use a variety of components like facial features of actors involved, scene understanding, visual aesthetics, action/situation description, and movie script to obtain an affective-rich representation to understand and perceive the scene. We use an LSTM-based learning model for emotion perception. To evaluate our method, we analyze and compare our performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE dataset, and observe an average of 10 − 15% increase in the performance over SOTA methods for all three datasets.

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Section: Theory Of Emotions: a Look Into Psychologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality

Mittal¹,

Mathur²,

Bera³

et al. 2021

Preprint

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“…The semantic and ontological representation of a verb is an event structure [2], [49]. In addition, the perception of causality can also evoke the perception of metaphors [50]. We have shown that Chinese radicals represent the most profiled element in an event structure; hence the eventive information encoded by radicals can contribute to metaphors detection and classification.…”

Section: Event Types and Syntactic Featuresmentioning

confidence: 99%

Metaphor Detection: Leveraging Culturally Grounded Eventive Information

Chen

Long

et al. 2019

IEEE Access

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Metaphors are compact packages of information with rich cultural background information. As one of the most powerful linguistic forms with non-literal meaning, metaphor detection in natural language processing can be both challenging and rewarding. We propose an innovative method for metaphor detection and classification leveraging culturally grounded eventive information. This culturally grounded information is organized based on ontological structure, which in turn facilitates further semantic processing of the result of our classification. As a culturally bound ontological system, the Chinese writing system has basic concepts organized according to semantic radicals, which are symbols containing rich eventive information that represent categorical concepts. This paper illustrates the basic design principles of applying ontological structures in metaphor detection by taking into account radicals representing body parts, instruments, materials, and movements. Our approach to leverage the eventive information of the Chinese writing system in metaphor detection is based on the fact that such information is available as an integral part of the writing system of any text. We hypothesize that eventive information can be accessed through the ''embodied'' source domain information represented by the radicals without syntactic processing or annotation. In terms of the theory of metaphor, we further hypothesize that eventive types in the embodied source domain maps to, and hence can help to predict, eventive meaning in the target domain of metaphor. Our studies show that the event information encoded in lexical items can facilitate classification of metaphoric events and identification of metaphors in Chinese texts effectively. We achieved improvements in Chinese metaphor detection over state-of-the-art approaches in our first classification experiment, and our proposed approach is shown to be generalizable in a second experiment involving new sets of characters with the same radicals.

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“…In this paper, the phrase "understanding the cause" refers to the understanding of the momentary associations between emotional states and the scene images in eyewear devices. It should be distinguished with the understanding of the emotions' causality[11] which is a different task.Proc. ACM Interact.…”

mentioning

confidence: 99%

Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

Zhao,

Chang,

et al. 2022

Preprint

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Emotion recognition in smart eyewear devices is highly valuable but challenging. One key limitation of previous works is that the expression-related information like facial or eye images is considered as the only emotional evidence. However, emotional status is not isolated; it is tightly associated with people's visual perceptions, especially those sentimental ones. However, little work has examined such associations to better illustrate the cause of different emotions. In this paper, we study the emotionship analysis problem in eyewear systems, an ambitious task that requires not only classifying the user's emotions but also semantically understanding the potential cause of such emotions. To this end, we devise EMOShip, a deep-learning-based eyewear system that can automatically detect the wearer's emotional status and simultaneously analyze its associations with semantic-level visual perceptions. Experimental studies with 20 participants demonstrate that, thanks to the emotionship awareness, EMOShip not only achieves superior emotion recognition accuracy over existing methods (80.2% vs. 69.4%), but also provides a valuable understanding of the cause of emotions. Pilot studies with 20 participants further motivate the potential use of EMOShip to empower emotion-aware applications, such as emotionship self-reflection and emotionship life-logging.CCS Concepts: • Human-centered computing → Mobile devices.

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Perceiving Causality in Character Perception: A Metaphorical Study of Causation in Film

Cited by 10 publications

References 27 publications

Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality

Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality

Metaphor Detection: Leveraging Culturally Grounded Eventive Information

Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

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